Both Big Data and Machine Learning (ML) are the latest buzzwords. But do they refer to the same? Can they be used interchangeably? Or are they different but connected in some way? They are different in terms of approach and application but they are not mutually exclusive. Meaning, their paths intersect and they are interdependent. While Artificial Intelligence training can make you an expert in ML concepts, learning Big Data Hadoop and its tools can help you master Big Data technologies. Let’s look at both the technologies and figure out how different or same they are.
What is Big Data?
Big Data is the term that refers to the humungous amount of data generated every second from various sources like social media, retail stores, government organizations, e-commerce sites, financial services, telecom, and so on. Some petabytes of data are accumulated every day contributing to the zettabytes of Big Data. It is defined by the 5 v’s as a standard – volume, veracity, velocity, variety, and value.
The Big Data technologies involve collecting the data, storing them across repositories, processing it, segmenting them, manipulate the data to make it usable, performing analysis, and then apply the analysis to make meaningful predictions.
What is Machine Learning?
Machine Learning, on the other hand, is the subset of Artificial Intelligence (AI). It is teaching the machines to perform human-like tasks such as thinking and acting like them and accomplish the activities, the humans cannot.
The machine learning technology is catching up and we are literally coexisting with them with/without our knowledge.
- The movie recommendation systems on Netflix, Amazon Prime are a good example of ML.
- The language support on Smartphones that predicts the word before you type-in is an example of ML.
- The tentative cab rides fare in Uber/Ola show even before the ride starts is also an instance of ML.
When do their paths intersect?
Big Data is of no use if the data is not processed, analyzed, and applied. When technologies like SQL, Apache Hadoop, Spark, Mahout, R, are used to treat the Big Data and the output is fed into the ML algorithms, that’s when Machine Learning happens.
The hidden patterns and extracting the information is performed by properly segregating the big data and tuning it for outliers and missing information.
Machine Learning, on the other hand, is feeding the trained data to the machines and anticipating the results for test data with the use of machine learning models.